library(Seurat)
## Attaching SeuratObject
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)
library(stringr)
library(tibble)
library(patchwork)
library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout

DE table

First we load the sister pair DE tables and filter for:

  • absolute avg_log2FC > 0.5 (~41% increase)

  • p_val_adj < 0.01

DE_list <- readRDS("~/spinal_cord_paper/data/Gg_ctrl_lumb_sis_markers.rds")

for (i in seq(DE_list)) {
    DE_list[[i]] <- DE_list[[i]] %>% 
    arrange(desc(avg_log2FC)) %>% 
    filter(abs(avg_log2FC) > 0.5) %>% 
    filter(p_val_adj < 0.01)
}

DE_table <- do.call(rbind, DE_list)
dim(DE_table)
## [1] 1510    8

delta pct distribution

par(mfrow = c(2,2))
hist(abs(DE_list[[1]]$delta_pct), breaks = 20)
abline(v = 0.1, lty = "dashed", col = "red")
hist(abs(DE_list[[2]]$delta_pct), breaks = 20)
abline(v = 0.1, lty = "dashed", col = "red")
hist(abs(DE_list[[4]]$delta_pct), breaks = 20)
abline(v = 0.1, lty = "dashed", col = "red")
hist(abs(DE_list[[5]]$delta_pct), breaks = 20)
abline(v = 0.1, lty = "dashed", col = "red")

Now we filter the DE lists for absolute delta percentage > 0.1.

for (i in seq(DE_list)) {
  DE_list[[i]] <- DE_list[[i]] %>% 
  filter(abs(delta_pct) > 0.1)
}

DE_table <- do.call(rbind, DE_list)
dim(DE_table)
## [1] 1113    8

Broad clusters

broad_order <- c("progenitors",
      "FP",
      "RP",
      "FP/RP",
      "neurons",
      "OPC",
      "MFOL",
      "pericytes",
      "microglia",
      "blood",
      "vasculature"
      )

Integrated data

Load the integrated control and poly data.

int_path <- "Gg_ctrl_lumb_int_seurat_250723"

my.se <- readRDS(paste0("~/spinal_cord_paper/data/", int_path, ".rds"))
  annot_int <- read.csv(list.files("~/spinal_cord_paper/annotations",
                               pattern = str_remove(int_path, "_seurat_\\d{6}"),
                               full.names = TRUE))
  
  if(length(table(annot_int$number)) != length(table(my.se$seurat_clusters))) {
     stop("Number of clusters must be identical!")
  }
  
  # rename for left join
  annot_int <- annot_int %>% 
    mutate(fine = paste(fine, number, sep = "_")) %>% 
    mutate(number = factor(number, levels = 1:nrow(annot_int))) %>% 
    rename(seurat_clusters = number)
  
  ord_levels <- annot_int$fine[order(match(annot_int$broad, broad_order))]
   
  # add cluster annotation to meta data
  my.se@meta.data <- my.se@meta.data %>% 
    rownames_to_column("rowname") %>% 
    left_join(annot_int, by = "seurat_clusters") %>% 
    mutate(fine = factor(fine, levels = ord_levels)) %>% 
    mutate(seurat_clusters = factor(seurat_clusters, levels = str_extract(ord_levels, "\\d{1,2}$"))) %>% 
    column_to_rownames("rowname")
  
  ctrl_poly_int_combined_labels <- readRDS("~/spinal_cord_paper/annotations/ctrl_lumb_int_combined_labels.rds")
  
  my.se <- AddMetaData(my.se, ctrl_poly_int_combined_labels)

DimPlot

DimPlot(
  my.se,
  group.by = "annot_sample",
  reduction = "tsne",
  label = TRUE,
  repel = TRUE
  ) +
  NoLegend()
## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

Cluster order

Get the cluster order from the spearman correlation heatmap of the control and poly integrated data. Then we filter for the neuronal clusters only.

corr_heatmap <- readRDS("~/spinal_cord_paper/output/heatmap_spearman_ctrl_lumb.rds")

#heatmap order
htmp_order <- data.frame("label" = corr_heatmap[["gtable"]]$grobs[[4]]$label) %>% 
  mutate(label = str_remove(label, "_int")) %>% 
  mutate(label_ordered = paste(str_sub(label,6 ,-1), str_sub(label, 1, 4), sep = "_"))

my.se@meta.data <- my.se@meta.data %>%
  mutate(annot_sample = factor(annot_sample, levels = htmp_order$label_ordered))

Idents(my.se) <- "annot_sample"

# filter for the neuronal clusters
my.se <- subset(my.se, idents = htmp_order$label_ordered[grepl("neurons|MN", htmp_order$label_ordered)])

DimPlot(
  my.se,
  group.by = "annot_sample",
  reduction = "tsne",
  label = TRUE,
  repel = TRUE
  ) +
  NoLegend()

my.se@active.assay <- "RNA"

Individual dot plots

# select top50 by log2FC 
for (i in seq(DE_list)) {
    DE_list[[i]] <- DE_list[[i]] %>%
    slice_max(order_by = abs(avg_log2FC), n = 50) %>% 
    arrange(desc(avg_log2FC))
}

p1 <- modplots::mDotPlot2(my.se,
                    group.by = "annot_sample", 
                    assay = "RNA",
                      # reverse order of DE genes so number one is on top
                    features = rev(DE_list[[1]]$Gene.stable.ID),
                    gnames = modplots::gnames,
          cols = c("lightgrey", "black")) +
    theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
    coord_flip() +
    xlab(names(DE_list)[1])
## 
p2 <- modplots::mDotPlot2(my.se,
                    group.by = "annot_sample",  
                    assay = "RNA",
                      # reverse order of DE genes so number one is on top
                    features = rev(DE_list[[2]]$Gene.stable.ID),
                    gnames = modplots::gnames,
          cols = c("lightgrey", "black")) +
    theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
    coord_flip() +
    xlab(names(DE_list)[2])

p3 <- modplots::mDotPlot2(my.se,
                    group.by = "annot_sample",  
                    assay = "RNA",
                      # reverse order of DE genes so number one is on top
                    features = rev(DE_list[[3]]$Gene.stable.ID),
                    gnames = modplots::gnames,
          cols = c("lightgrey", "black")) +
    theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
    coord_flip() +
    xlab(names(DE_list)[3])

p4 <- modplots::mDotPlot2(my.se,
                    group.by = "annot_sample",  
                    assay = "RNA",
                      # reverse order of DE genes so number one is on top
                    features = rev(DE_list[[4]]$Gene.stable.ID),
                    gnames = modplots::gnames,
          cols = c("lightgrey", "black")) +
    theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
    coord_flip() +
    xlab(names(DE_list)[4])

p5 <- modplots::mDotPlot2(my.se,
                    group.by = "annot_sample",  
                    assay = "RNA",
                      # reverse order of DE genes so number one is on top
                    features = rev(DE_list[[5]]$Gene.stable.ID),
                    gnames = modplots::gnames,
          cols = c("lightgrey", "black")) +
    theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
    coord_flip() +
    xlab(names(DE_list)[5])
pdf("~/spinal_cord_paper/figures/ctrl_lumb_dotplot_individual.pdf", height = 13, width = 20)
(p1 + p2 + p3 + p4 + p5) + plot_layout(guides = "collect", nrow = 1)
dev.off()
## png 
##   2

Volcanoplots

p.adj <- 0.01
l2fc <- 0.5

# select top50 by log2FC 
for (i in seq(DE_list)) {
    DE_list[[i]] <- DE_list[[i]] %>% 
    mutate(delta_pct_sign = case_when(
      delta_pct < 0 ~ "-",
      delta_pct > 0 ~ "+",
      delta_pct == 0 ~ "0"
    ))
}
 

toplot <- do.call(rbind, DE_list) %>% 
  rownames_to_column("contrast") %>% 
  mutate(contrast = str_remove(contrast, "\\.\\d{1,2}")) %>% 
  mutate(contrast = str_replace_all(contrast, " ", "_")) %>% 
  filter(!grepl("^HOX", Gene.name)) # remove hox genes

volplot <- ggplot(data = toplot,
       aes(x = avg_log2FC,
           y = -log10(p_val_adj),
           label = Gene.name,
           color = delta_pct_sign,
           size = abs(delta_pct)
       )) +
  geom_point(shape = 21) +
  geom_hline(yintercept = -log10(p.adj), linetype = "dashed") +
  geom_vline(xintercept = c(-l2fc,l2fc), linetype = "dashed") +
  scale_color_manual(values = c("#419c73", "black")) +
  scale_size_continuous(range = c(0.5, 4)) +
  facet_wrap("contrast", ncol = 5, scales = "free") +
  ylab("-log10(padj)") +
  theme_bw()

ggplotly(volplot)
pdf("~/spinal_cord_paper/figures/Fig_4_volcanoplots.pdf", width = 15, height = 15)
(volplot +
  ggrepel::geom_text_repel(size = 3, color = "black"))
## Warning: ggrepel: 5 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 11 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 7 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider increasing max.overlaps
## ggrepel: 6 unlabeled data points (too many overlaps). Consider increasing max.overlaps
## Warning: ggrepel: 5 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 29 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Date and time of Rendering
Sys.time()
## [1] "2024-07-26 16:51:02 CEST"
sessionInfo()
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
## 
## Matrix products: default
## BLAS/LAPACK: /scicore/soft/apps/OpenBLAS/0.3.1-GCC-7.3.0-2.30/lib/libopenblas_sandybridgep-r0.3.1.so
## 
## locale:
## [1] en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] plotly_4.10.0      patchwork_1.1.1    tibble_3.1.8       stringr_1.4.0     
## [5] ggplot2_3.3.3      dplyr_1.0.10       SeuratObject_4.0.2 Seurat_4.0.5      
## 
## loaded via a namespace (and not attached):
##   [1] plyr_1.8.6                  igraph_1.2.6               
##   [3] lazyeval_0.2.2              sp_1.4-5                   
##   [5] splines_4.1.0               crosstalk_1.1.1            
##   [7] listenv_0.8.0               scattermore_0.7            
##   [9] GenomeInfoDb_1.28.0         digest_0.6.27              
##  [11] htmltools_0.5.1.1           fansi_0.5.0                
##  [13] magrittr_2.0.1              memoise_2.0.0              
##  [15] tensor_1.5                  cluster_2.1.2              
##  [17] ROCR_1.0-11                 globals_0.16.2             
##  [19] Biostrings_2.60.0           matrixStats_0.58.0         
##  [21] modplots_1.0.0              spatstat.sparse_3.0-0      
##  [23] colorspace_2.0-1            blob_1.2.1                 
##  [25] ggrepel_0.9.1               xfun_0.34                  
##  [27] RCurl_1.98-1.3              crayon_1.4.1               
##  [29] jsonlite_1.7.2              spatstat.data_3.0-0        
##  [31] survival_3.2-11             zoo_1.8-9                  
##  [33] glue_1.6.2                  polyclip_1.10-0            
##  [35] gtable_0.3.0                zlibbioc_1.38.0            
##  [37] XVector_0.32.0              leiden_0.3.9               
##  [39] DelayedArray_0.18.0         future.apply_1.7.0         
##  [41] BiocGenerics_0.38.0         abind_1.4-5                
##  [43] scales_1.1.1                pheatmap_1.0.12            
##  [45] DBI_1.1.1                   miniUI_0.1.1.1             
##  [47] Rcpp_1.0.7                  viridisLite_0.4.0          
##  [49] xtable_1.8-4                reticulate_1.22            
##  [51] spatstat.core_2.1-2         bit_4.0.4                  
##  [53] stats4_4.1.0                htmlwidgets_1.5.3          
##  [55] httr_1.4.2                  RColorBrewer_1.1-2         
##  [57] ellipsis_0.3.2              ica_1.0-2                  
##  [59] pkgconfig_2.0.3             farver_2.1.0               
##  [61] sass_0.4.0                  uwot_0.1.10                
##  [63] deldir_1.0-6                utf8_1.2.1                 
##  [65] tidyselect_1.2.0            labeling_0.4.2             
##  [67] rlang_1.0.6                 reshape2_1.4.4             
##  [69] later_1.2.0                 AnnotationDbi_1.54.0       
##  [71] munsell_0.5.0               tools_4.1.0                
##  [73] cachem_1.0.5                cli_3.4.1                  
##  [75] generics_0.1.3              RSQLite_2.2.7              
##  [77] ggridges_0.5.3              org.Gg.eg.db_3.13.0        
##  [79] evaluate_0.20               fastmap_1.1.0              
##  [81] yaml_2.2.1                  goftest_1.2-2              
##  [83] knitr_1.41                  bit64_4.0.5                
##  [85] fitdistrplus_1.1-6          purrr_0.3.4                
##  [87] RANN_2.6.1                  KEGGREST_1.32.0            
##  [89] pbapply_1.4-3               future_1.30.0              
##  [91] nlme_3.1-152                mime_0.10                  
##  [93] compiler_4.1.0              rstudioapi_0.13            
##  [95] png_0.1-7                   spatstat.utils_3.0-1       
##  [97] bslib_0.2.5.1               stringi_1.6.2              
##  [99] highr_0.9                   lattice_0.20-44            
## [101] Matrix_1.3-3                vctrs_0.5.1                
## [103] pillar_1.8.1                lifecycle_1.0.3            
## [105] spatstat.geom_3.0-3         lmtest_0.9-38              
## [107] jquerylib_0.1.4             RcppAnnoy_0.0.19           
## [109] bitops_1.0-7                data.table_1.14.0          
## [111] cowplot_1.1.1               irlba_2.3.3                
## [113] GenomicRanges_1.44.0        httpuv_1.6.1               
## [115] R6_2.5.0                    promises_1.2.0.1           
## [117] KernSmooth_2.23-20          gridExtra_2.3              
## [119] IRanges_2.26.0              parallelly_1.33.0          
## [121] codetools_0.2-18            MASS_7.3-54                
## [123] assertthat_0.2.1            SummarizedExperiment_1.22.0
## [125] withr_2.4.2                 sctransform_0.3.3          
## [127] GenomeInfoDbData_1.2.6      S4Vectors_0.30.0           
## [129] mgcv_1.8-35                 parallel_4.1.0             
## [131] grid_4.1.0                  rpart_4.1-15               
## [133] tidyr_1.1.3                 rmarkdown_2.17             
## [135] MatrixGenerics_1.4.0        Rtsne_0.15                 
## [137] Biobase_2.52.0              shiny_1.6.0